# Deep Learning Image Enhancement for Point of Care Ultrasound

> **NIH NIH F30** · DUKE UNIVERSITY · 2021 · $37,475

## Abstract

Project Summary
Ultrasound has many clinical applications due to it’s non-invasive, non-ionizing, and real-time imaging
properties. However, ultrasound still relies heavily on operator skills for image acquisition and interpretation.
Operator skill is especially challenged in overweight and obese patient populations where imaging artifacts
such as acoustic clutter are more prominent and decrease anatomical conspicuity. To decrease the
interpretation burden faced by operators, we aim to develop a deep learning framework for real-time
acoustic clutter artifact suppression.
 We generate preliminary in silico training data using a configurable cloud-compute tool that scales to
an 8000 CPU cluster. This tool is ideal for deep learning methods as it significantly speeds up the
turnaround time for simulating unique ultrasound acquisition configurations enabling data generation in days
as opposed to months. In this project, we will open-source our cloud-compute simulations tools, improve our
current in silico data model of acoustic clutter by incorporating human abdominal wall tissue information
from medical CT scans, and assess our clutter correction model’s performance on in vivo data.
 To translate our model’s results for medical provider interpretation, image post-processing is
necessary. In our recently published work, MimickNet, we use deep learning methods to successfully
approximate post-processing algorithms found on some of the best clinical-grade ultrasound scanners. We
propose extending MimickNet to incorporate post-processing approximations for anatomy-specific use
cases such as cardiac and vascular imaging. This will provide more off-the-shelf tooling for researchers to
translate their algorithmic research into image forms familiar to providers, thus easing clinical translation.
 Lastly, portable ultrasound hardware has significantly decreased in cost, enabling the widespread
use of mobile point-of-care ultrasound (POCUS). Since many consumer devices contain hardware
accelerators specific for deep learning applications, there is an opportunity to correct ultrasound artifacts in
real-time, even while constrained to mobile hardware. Our preliminary data show that beamforming
operations and MimickNet can run at > 100 frames-per-second on an NVIDIA P100 GPU. We propose
developing a framework to transfer our image processing pipeline completely onto mobile hardware
accelerators. This work will enable translating novel image processing algorithms as easy as downloading
software.
 Our work in developing a deep learning framework for POCUS systems covers the full image
reconstruction pipeline from simulated data to producing a clinical-grade image familiar to providers. This
framework will provide a rapid translational path for improving ultrasound imaging quality on cheap and
widely available mobile hardware.

## Key facts

- **NIH application ID:** 10312492
- **Project number:** 1F30HL156547-01A1
- **Recipient organization:** DUKE UNIVERSITY
- **Principal Investigator:** Ouwen Huang
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $37,475
- **Award type:** 1
- **Project period:** 2021-09-03 → 2025-11-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10312492

## Citation

> US National Institutes of Health, RePORTER application 10312492, Deep Learning Image Enhancement for Point of Care Ultrasound (1F30HL156547-01A1). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10312492. Licensed CC0.

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